@article{Daly2017b,
title = {Personalised, Multi-modal, Affective State Detection for Hybrid Brain-Computer Music Interfacing},
author = {Ian Daly and Duncan Williams and Asad Malik and James Weaver and Alexis Kirke and Faustina Hwang and Eduardo Miranda and Slawomir J. Nasuto},
year = {2017},
date = {2017-10-08},
journal = {IEEE Transactions on Affective Computing},
abstract = {Brain-computer music interfaces (BCMIs) may be used to modulate affective states, with applications in music therapy, composition, and entertainment. However, for such systems to work they need to be able to reliably detect their user’s current affective state.
We present a method for personalised affective state detection for use in BCMI. We compare it to a population-based detection method trained on 17 users and demonstrate that personalised affective state detection is significantly (p < 0:01) more accurate, with average improvements in accuracy of 10.2% for valence and 9.3% for arousal. We also compare a hybrid BCMI (a BCMI that combines physiological signals with neurological signals) to a conventional BCMI design
one based upon the use of only EEG features) and demonstrate that the hybrid design results in a significant (p < 0:01) 6.2% improvement in performance for arousal classification and a significant (p < 0:01) 5.9% improvement for valence classification.},
keywords = {Affective computing, BCI, Classification, Feature selection, Machine learning},
pubstate = {published},
tppubtype = {article}
}

Brain-computer music interfaces (BCMIs) may be used to modulate affective states, with applications in music therapy, composition, and entertainment. However, for such systems to work they need to be able to reliably detect their user’s current affective state.
We present a method for personalised affective state detection for use in BCMI. We compare it to a population-based detection method trained on 17 users and demonstrate that personalised affective state detection is significantly (p < 0:01) more accurate, with average improvements in accuracy of 10.2% for valence and 9.3% for arousal. We also compare a hybrid BCMI (a BCMI that combines physiological signals with neurological signals) to a conventional BCMI design
one based upon the use of only EEG features) and demonstrate that the hybrid design results in a significant (p < 0:01) 6.2% improvement in performance for arousal classification and a significant (p < 0:01) 5.9% improvement for valence classification.

The electroencephalogram (EEG) may be described by a large number of different feature types and automated feature selection methods are needed in order to reliably identify features which correlate with continuous independent variables.

New method

A method is presented for the automated identification of features that differentiate two or more groups in neurological datasets based upon a spectral decomposition of the feature set. Furthermore, the method is able to identify features that relate to continuous independent variables.

Results

The proposed method is first evaluated on synthetic EEG datasets and observed to reliably identify the correct features. The method is then applied to EEG recorded during a music listening task and is observed to automatically identify neural correlates of music tempo changes similar to neural correlates identified in a previous study. Finally, the method is applied to identify neural correlates of music-induced affective states. The identified neural correlates reside primarily over the frontal cortex and are consistent with widely reported neural correlates of emotions.

Comparison with existing methods

The proposed method is compared to the state-of-the-art methods of canonical correlation analysis and common spatial patterns, in order to identify features differentiating synthetic event-related potentials of different amplitudes and is observed to exhibit greater performance as the number of unique groups in the dataset increases.

The electroencephalogram (EEG) may be described by a large number of different feature types and automated feature selection methods are needed in order to reliably identify features which correlate with continuous independent variables.

New method

A method is presented for the automated identification of features that differentiate two or more groups in neurological datasets based upon a spectral decomposition of the feature set. Furthermore, the method is able to identify features that relate to continuous independent variables.

Results

The proposed method is first evaluated on synthetic EEG datasets and observed to reliably identify the correct features. The method is then applied to EEG recorded during a music listening task and is observed to automatically identify neural correlates of music tempo changes similar to neural correlates identified in a previous study. Finally, the method is applied to identify neural correlates of music-induced affective states. The identified neural correlates reside primarily over the frontal cortex and are consistent with widely reported neural correlates of emotions.

Comparison with existing methods

The proposed method is compared to the state-of-the-art methods of canonical correlation analysis and common spatial patterns, in order to identify features differentiating synthetic event-related potentials of different amplitudes and is observed to exhibit greater performance as the number of unique groups in the dataset increases.

Conclusions

The proposed method is able to identify neural correlates of continuous variables in EEG datasets and is shown to outperform canonical correlation analysis and common spatial patterns.

Brain computer Interface (BCI) development encapsulates three basic processes: data acquisition, data processing, and device control. Since the start of the millennium the BCI development cycle has undergone a metamorphosis. This is mainly due to the increased popularity of BCI applications in both commercial and research circles. One of the focuses of BCI research is to bridge the gap between laboratory research and commercial applications using this technology. A vast variety of new approaches are being employed for BCI development ranging from novel paradigms, such as simultaneous acquisitions, through to asynchronous BCI control. The strategic usage of computational techniques, comprising the heart of the BCI system, underwrites this vast range of approaches. This chapter discusses these computational strategies and translational techniques including dimensionality reduction, feature extraction, feature selection, and classification techniques.

@phdthesis{Daly2011a,
title = {Phase Synchronisation in Brain Computer Interfacing},
author = {Ian Daly},
url = {http://www.iandaly.co.uk/publications/thesis/Phase_Synchronisation_in_Brain_Computer_Interfacing.pdf},
year = {2011},
date = {2011-07-01},
pages = {1-262},
address = {University of Reading},
school = {School of Systems Engineering},
abstract = {Brain Computer Interfaces (BCIs) are an emerging area of research combining the Neuroscience, Computer Science, Engineering, Mathematics, Human Computer Interaction and Psychology research fields. A BCI enables an individual to exert control of a computer without activation of the efferent nervous system or the muscles. This allows individuals suffering with partial or complete paralysis and associated conditions which prevent muscle movement to control a computer and hence communicate and exert control over their environment.

This thesis first investigates tools for automatically removing artifacts from the Electroencephalogram (EEG), a signal commonly used in the control a BCI. Tools for measuring inter-regional connectivity patterns within the brain via phase synchronisation are then evaluated and extended to provide novel measures of inter-regional connectivity across the entire cortex.

Feature selection approaches are then introduced and evaluated before being applied to select good feature sets for the discrimination of connectivity patterns. These approaches are compared to Markov modelling approaches which model
and classify temporal dependencies in the data.

Brain Computer Interfaces (BCIs) are an emerging area of research combining the Neuroscience, Computer Science, Engineering, Mathematics, Human Computer Interaction and Psychology research fields. A BCI enables an individual to exert control of a computer without activation of the efferent nervous system or the muscles. This allows individuals suffering with partial or complete paralysis and associated conditions which prevent muscle movement to control a computer and hence communicate and exert control over their environment.

This thesis first investigates tools for automatically removing artifacts from the Electroencephalogram (EEG), a signal commonly used in the control a BCI. Tools for measuring inter-regional connectivity patterns within the brain via phase synchronisation are then evaluated and extended to provide novel measures of inter-regional connectivity across the entire cortex.

Feature selection approaches are then introduced and evaluated before being applied to select good feature sets for the discrimination of connectivity patterns. These approaches are compared to Markov modelling approaches which model
and classify temporal dependencies in the data.

The resulting tool-set is applied to a novel BCI control paradigm based upon the detection of single finger taps. It is demonstrated that the connectivity features produce significantly better classification accuracies than can be achieved using conventional features traditionally applied in BCI.